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Dietary phytochemical index and the risk of cancer: A systematic review and meta-analysis

Background Recently, the association between dietary phytochemical index (DPI) and the risk of cancer has been the focus of researchers, however, this possible association has not been fully understood. The current meta-analysis aimed to assess the relationship between DPI and the risk of cancers. Methods A literature search by the main keywords such as “dietary phytochemical index”, “DPI”, and “cancer” was completed using Scopus, PubMed, and Web of Science up to December 2024 and references of retrieved relevant articles. Observational studies examining the association between the DPI and the risk of cancers were included. The reported odds ratio (OR) with a 95% confidence interval (CI) for each study was converted into log OR, and their standard deviation was calculated. Then to compute the pooled OR, the random-effects model with inverse variance weighting method was performed. Results Nine case-control studies were included in the present meta-analysis. The sample size ranged from 120 to 851 with an age range from 18 to 75 years. The pooled results indicate an inverse association between DPI and the risk of all cancers (OR: 0.40; 95% CI: 0.29–0.54, I2 = 0.00%; P-value < 0.001). Also, subgroup analysis indicated that higher a DPI score is related to the decreased risk of breast cancer (OR: 0.38; 95% CI: 0.26–0.55, I2 = 0.00%; P-value < 0.001) and pooled non-breast cancer including glioma, prostate, and colorectal cancers (OR: 0.43; 95% CI: 0.27–0.71, I2 = 0.00%; P-value = 0.001). Conclusions The results of the current meta-analysis revealed that the higher DPI score is associated with a decreased odds of cancers. Large-scale cohort studies are recommended to validate the findings presented in the current study.

Viologen Covalent Organic Framework Mediates Near-Infrared Light-Induced Electron Transfer for Catalytic Oxidative Coupling Reactions

Assessing competitiveness and complementarity in agricultural trade between China and Cambodia pre-pandemic and post-pandemic

The concepts of trade competitiveness and complementarity function as key indicators for assessing the strengths and weaknesses of a nation’s export profile, as well as predicting future trade trajectories. Analyzing the levels of competitiveness and complementarity within the agricultural sectors of Cambodia and China is imperative for understanding and comparing their respective competitive advantages. This study utilizes quantitative research methodologies to analyze the agricultural trade dynamics between China and Cambodia before and after the pandemic. Specifically, the research involves the calculation of the revealed comparative advantage (RCA) index, the trade complementarity index (TCI), the trade intensity index (TII), and the Grubel-Lloyd (GL) index, covering the period from 2017 to 2022. The data is from the UN Commodity Trade Statistics Database through the HS code 1-24. The study used a three-year period as a short research phase and applied two phases to compare pre-pandemic and post-pandemic. The empirical results demonstrated that China and Cambodia both exhibit a strong comparative advantage in exporting specific items. From 2017 to 2019, China has comparative advantages in exporting HS items 05,13 and 16, and Cambodia has comparative advantages in exporting HS items 10,11 and 17. From 2020 to 2022, China has a comparative advantage in exporting HS item 13, and Cambodia has a comparative advantage in exporting HS item 10. Both China and Cambodia have specific complementary agricultural items in bilateral agricultural exports from 2017 to 2019, but China does not have complementary exports in its exports and Cambodia’s imports from 2020 to 2022. The trade intensity index underscores the advantages of their bilateral trade in the direction of China’s exports to Cambodia. Inter-industry advantages are identified in specific agricultural products. This study is beneficial for the Cambodian government and relevant stakeholders in formulating sustainable policies to promote agricultural trade after pandemic. The insights derived from these indices will furnish a rigorous foundation upon which the Cambodian government can develop and implement evidence-based sustainable strategies to optimize and enhance its agricultural trade performance.

Stabilizing the Unstable: Enhancing OER Durability with 3<i>d</i>-Orbital Transition Metal Multielemental Alloy Nanoparticles by Atomically Dispersed 4<i>d</i>-Orbital Pd for a 100-Fold Extended Lifetime

Efficient quantification of Parkinson’s disease severity using augmented time-series data

The diagnosis of Parkinson’s disease relies heavily on the subjective assessment of physicians, which depends on their individual experience and training, potentially leading to inconsistent diagnostic results. Therefore, developing an objective and efficient diagnostic method is essential to improve the accuracy and timeliness of Parkinson’s disease diagnosis. In this study, we utilized the PhysioNet dataset, a time-series dataset comprising data from 93 Parkinson’s patients and 73 healthy individuals. The dataset contains vertical ground reaction forces recorded from 16 sensors (8 per foot) during a 2-minute test at a sampling rate of 100 Hz. To address challenges such as limited dataset size, high labeling noise, and high intra-class variability, we performed data preprocessing and applied various data augmentation techniques, including jittering, scaling, rotation, permutation, magnitude warping, time warping, cropping, and linear residuals. These methods were evaluated using one-dimensional-convolutional neural network (1D-ConvNet) and one-dimensional Transformer networks. By conducting 10-fold cross-validation, we observed significant improvements in classification performance. The best data augmentation strategy achieved 90.8% accuracy, 92.0% precision, 91.0% recall, and a 91.0% F1 score in assessing disease severity. These results highlight the importance of selecting appropriate data augmentation techniques for time-series data to improve model generalization and diagnostic reliability, while also offering new insights for researchers working with sensor device data. Our results demonstrate that data-enhanced methods can significantly boost the performance of machine-learning models in the field of Parkinson’s disease diagnosis.

Diversity Synthesis Using Glutarimides as Rhodium Carbene Precursors in Enantioselective C–H Functionalization and Cyclopropanation

Oral health-related knowledge, practice, and utilization of dental services among pregnant women in Riyadh, Saudi Arabia

Background Promoting and maintaining appropriate oral hygiene practices in pregnant women is recommended as an integral component of prenatal care to ensure healthy pregnancies and positive birth outcomes. This study aimed to assess the oral health-related knowledge, practices, and utilization of dental services among pregnant women in Riyadh, Saudi Arabia. Methods A cross-sectional survey of 1120 pregnant women was conducted in both government and private maternal hospitals from October 2022 to January 2023. A self-administered questionnaire was developed and validated, comprising 37 closed-ended questions. Data analysis involved descriptive analysis, Kruskal-Wallis test, and Spearman correlation coefficient. Multiple linear and multivariable logistic regression analyses were carried out using socio-demographic variables as predictors for knowledge, practices, and utilization of dental services. Level of significance was set at P ≤  0.05. Results Majority (37.2%) of the pregnant women were in the 30-34 years age group. The majority had limited oral health knowledge (59.7% very poor, 23.8% poor), and 66.4% had poor oral health practices. Dental service utilization was suboptimal, with 39.6% never seeking dental care during pregnancy. A positive correlation was seen among knowledge, practices, and utilization of dental services. Multiple linear regression revealed that both knowledge and practices scores were significantly associated with education and occupation; and utilization scores with occupation. Conclusion This study highlights the need for enhancing oral health knowledge, practices, and utilization of dental services among pregnant women in Riyadh. Bridging knowledge and access gaps can enhance the oral and overall health outcomes of mothers and their infants.

RETRACTED: Evaluation of all-for-one tourism development level: Evidence from Xinjiang production and construction corps, China

All-for-one tourism represents a pivotal strategy to facilitate the transformation and enhancement of the tourism sector and promote the coordinated development of the economy and society within the contemporary context of China. Since its introduction by the Ministry of Culture and Tourism in 2016, governments at all levels have actively promoted the development of all-for-one tourism with remarkable results. Xinjiang Production and Construction Corps (XPCC), a unique governance system in Xinjiang, is rich in natural and cultural tourism resources and has achieved specific results in developing all-for-one tourism. Nevertheless, it also faces the outstanding problems of a low level of development and insufficient motivation. Therefore, this study focused on the XPCC as the research area and established an evaluation index system to assess the level of all-for-one tourism development across three dimensions: tourism potential, tourism benefit, and tourism format. Subsequently, a comprehensive analysis was conducted regarding the development level, spatial distribution characteristics, the coupling and coordination mechanisms among these dimensions, and the classification of tourism destinations. The results show that (1) XPCC’s four major regions have unbalanced all-for-one tourism development, revealing disparities among divisions. (2) Spatial analysis shows that the Tianshan North Slope is advantageous, while southern Xinjiang and border areas are disadvantageous. (3) The coupling degree spans four stages, with significant differences in spatial distribution. (4) Tourism destinations can be categorized into three types: industrial integration-driven, economic and social-driven, and advantageous resource-driven. Accordingly, the optimization development strategies for all-for-one tourism in the XPCC were proposed: (1) integrating resources to optimize supply, (2) enhancing infrastructure to improve services, (3) deepening industrial integration, (4) strengthening brand building, (5) promoting regional cooperation. These strategies provide insights for optimizing all-for-one tourism development in similar regions.

First unambiguous record of pneumaticity in the axial skeleton of alvarezsaurians (Theropoda: Coelurosauria)

Bonapartenykus ultimus is an alvarezsaurid theropod from the Upper Cretaceous of Patagonia, Argentina. This species is represented by the holotype specimen and several referred specimens, many of which have pneumatic structures. Pneumaticity involves the invasion of the interior of the skeleton by means of air sac diverticula. Such invasion occurs from cortical openings (foramina) that communicate with internal air spaces. Despite previous studies on pneumatic structures in theropod specimens, there are no studies focusing on the family Alvarezsauridae. Here we address this gap by presenting the first contribution focusing on the pneumatic features of an alvarezsaurid theropod using both external skeletal anatomy as well as computed tomographic images showing internal details. The specimens studied show that the axial skeleton of B. ultimus was invaded by pneumatic structures, reaching the middle section of the tail. Our study suggests that pneumaticity among alvarezsaurids did not have a linear evolutionary trajectory, but instead shows a more random pattern of variability. This study is an important first step that paves the way for future studies to uncover the extent of pneumatic invasion among alvarezsaurids and its macroevolutionary implications.

Divergent psychological stress response patterns to the COVID-19 pandemic in psychiatric patients with vs. without PTSD: A real-world exploratory study

The COVID-19 pandemic has been shown to increase psychological burden and requires efficient coping strategies to maintain mental health. In particular, it remains unclear which pandemic-related stress response pattern occurs in pre-existing posttraumatic stress disorder (PTSD) during the pandemic – at the same time these patients potentially exhibit dysfunctional coping of artificially generated psychosocial stressors. To analyze this so far widely unconsidered pandemic-related stress response in PTSD, this study longitudinally measured psychosocial burden and adjustment disorder (AD) symptom load in 14 patients with a primary or secondary diagnosis of PTSD vs. a cohort of 145 psychiatric patients without PTSD. The previously established Goettingen psychosocial Burden and Symptom Inventory (Goe-BSI) was used. Patients were interviewed at the end of the first (April/May 2020) and the second nationwide lockdown in Germany (November/December 2020). In our convenience sample, psychiatric disorders were diagnosed by patients’ treating clinicians prior to study inclusion. Psychosocial burden and AD symptom load were significantly higher in patients with PTSD than in patients without PTSD over the course of the pandemic (both p = .005). Moreover, explorative analysis of Goe-BSI-assessed general psychiatric symptoms did not reveal changes during the pandemic in patients with PTSD. In sum, we provide preliminary evidence that, in relation to psychiatric patients without PTSD, those with PTSD might experience a higher pandemic-related burden and might thus cope less efficiently with this enduring real-world stressor. This study is limited inter alia by the small sample size and by the underrepresentation of some psychiatric diagnoses.

Clinician perspectives on virtual reality use in physical therapy practice in the United States

The primary goal of physical rehabilitation is to assess movement impairments and restore function to improve overall quality of life. Virtual reality (VR) may provide the optimal environment to promote these goals due to its motivating and modifiable nature which can be difficult to accomplish through traditional real-world therapeutic methods. Current research of VR for rehabilitation has demonstrated that VR interventions can produce clinically meaningful change in motor outcomes. Despite this, adoption and usage of VR by physical therapy professionals is unclear due to the limited research in this area. Thus, the purpose of this study was to identify the current usage and perspectives of VR in physical rehabilitation among physical therapy professionals. Physical Therapists (PTs) and Physical Therapist Assistants (PTAs) in the United States were recruited to participate in this survey-based study. A total of N = 658 participants completed the survey, which consisted of demographic information followed by the Assessing Determinants Of Prospective Take-up of Virtual Reality (ADOPT-VR2) survey that assesses 12 constructs (e.g., Attitudes, Perceived Usefulness, Facilitating Conditions and Barriers) related to the use of VR in clinical settings. Most respondents reported not using VR in clinical practice (n = 611; 92.9%). For all respondents, the constructs of Attitudes, Perceived Ease of Use, Compatibility, Client Influence, and Self-Efficacy were found to statistically contribute to the prediction of Behavioral Intention to use VR (p &lt; .05). However, for those clinicians using VR in their clinical practice, Superior Influence and Perceived Behavioral Control were significant positive predictors for intention to use VR. Future investigation should aim to test strategies to target these factors significantly influencing VR use to further address the gap between evidence and clinical practice in the use of VR for physical rehabilitation interventions.

Physicochemical Attributes of Tomatoes after Different Forms of Harvesting and Transportation for Industrial Processing

Background The mechanized harvesting and transportation of tomatoes can significantly impact their physicochemical characteristics, affecting quality and industrial processing efficiency. This study aimed to evaluate the effects of harvesting methods and transportation conditions on the firmness, titratable acidity (TA), total soluble solids (TSS – °Brix), pH, and percentage of loss of fresh mass (LFM) of tomatoes intended for industrial use. Methods The mechanized harvesting and transportation of tomatoes can significantly impact their physicochemical characteristics, affecting quality and industrial processing efficiency. This study aimed to evaluate the effects of harvesting methods and transportation conditions on the firmness, TA, TSS (°Brix), pH, and percentage of LFM in tomatoes intended for industrial use. Results Mechanized harvesting reduced tomato firmness, TA, pH, and increased mass loss. Manual harvesting resulted in 29.7% greater firmness than mechanical harvesting. The vibration effects varied depending on the floor and direction within the container, but container type did not significantly influence tomato quality. The position of the fruit in the transport medium affected firmness, with tomatoes at the rear exhibiting greater firmness and lower quality loss. Depth was negatively correlated with firmness, LFM, TA, and pH. The LFM increased with longer unloading times. Conclusion The findings highlight the need for improved handling and logistics strategies in the tomato production chain to reduce quality deterioration during harvesting and transportation. Effective interventions can minimize economic losses and increase industrial processing efficiency. Additionally, the results of this study suggest that laboratory models that use equipment such as shakers can replicate these effects for other bulk-transported crops, including fresh fruits and tubers.

Retraction: Targeted non AR mediated smart delivery of abiraterone to the prostate cancer

Correction: More on mobility and sedentism: Changes in adaptation from Upper Paleolithic to Incipient Jomon, Tanegashima Island, southern Japan

“My drinking was way worse during the pandemic”: A qualitative analysis of contextual and individual factors impacting alcohol use during the COVID-19 pandemic

Background Evidence shows that alcohol use in the United States increased during the COVID-19 pandemic. This primarily quantitative work has not examined how the unique context of the COVID-19 pandemic in the U.S. may have shaped motivations for alcohol use during this crisis. To address this knowledge gap, we conducted an analysis of qualitative data from in-depth interviews examining people’s motivations for using alcohol during the COVID-19 pandemic. Methods Participants (N = 26) were derived from those who completed all three waves of the National Alcohol Survey COVID Cohort- a longitudinal population-based survey of non-institutionalized U.S. adults. Interviews were conducted from April-July 2022 over Zoom. Interview transcripts were analyzed iteratively via codebook thematic analysis. Results Participants described how both contextual and individual level factors resulted in increased alcohol use. Contextual factors included reductions in barriers to alcohol use and increases in alcohol availability and accessibility. Individual level factors included using alcohol to regulate emotions (e.g., to alleviate boredom and as an end of day “reward”) and to celebrate reconnecting with loved ones once social distancing restrictions began to ease. Conclusions Our results suggest that environmental forces may have interacted with individuals’ emotions to shape alcohol use motivations during the COVID-19 pandemic. This work helps to contextualize quantitative research on changes in alcohol use observed during the COVID-19 pandemic. More research is needed to determine the long-term impacts of pandemic era changes in alcohol use. It is also necessary for future studies to examine how such impacts may manifest differently across the U.S. population (e.g., among racial/ethnic minority individuals).

Evaluating how different balancing data techniques impact on prediction of premature birth using machine learning models

Premature birth can be defined as birth before 37 weeks of gestation, which is a significant global health issue, being the main cause for neonatal deaths. In this work, we evaluate machine learning models for predicting premature birth using Brazilian sociodemographic and obstetric data, focusing on the challenge of data imbalance, a common problem that can lead to biased predictions. We evaluate five data balancing techniques: Undersampling, Oversampling, and three Hybridsampling configurations where the minority class was increased by factors 2, 3, and 4. The machine learning models, including Decision Tree, Random Forest, and AdaBoost, are trained and evaluated on a dataset of over 483,000 cases. The use of the Hybridsampling approach resulted in an accuracy of 70%, a recall of 64%, and a precision of 74% in the Decision Tree model. Results show that Hybridsampling techniques significantly improves models’ performance compared to Undersampling and Oversampling, highlighting the importance of a proper data balancing in predictive models for preterm birth. The relevance of our work is particularly significant for the Brazilian Unified Health System (SUS). By improving the accuracy of premature birth predictions, our models could assist healthcare providers in identifying at-risk pregnancies earlier, allowing for timely interventions. This integration could enhance maternal and neonatal care, reduce the incidence of preterm births, and potentially decrease neonatal mortality, especially in underserved regions.

Various optimization algorithms for efficient placement and sizing of photovoltaic distributed generations in different networks

Recent research has concentrated on emphasizing the significance of incorporating renewable distributed generations (RDGs), like photovoltaic (PV) and wind turbines (WTs), into the distribution system to address issues related to distributed generation (DG) allocation. The key implications of integrating RDGs include the improvement of voltage profiles and the minimization of power losses. Various optimization techniques, namely Salp Swarm Algorithm (SSA), Marine Predictor Algorithm (MPA), Grey Wolf Optimizer (GWO), Improved Grey Wolf Optimizer (IGWO), and Seagull Optimization Algorithm (SOA), have been applied to achieve optimal allocation and sizing of RDGs in radial distributed systems (RDS). The present paper is structured in two phases. In the initial phase, the Loss Sensitivity Factor (LSF) is employed to identify the most suitable nodes for integrating RDGs. In the second phase, within the selected candidate nodes from the first phase, the optimal location and capacity of RDGs are determined. Additionally, a comprehensive comparison of the proposed optimization methods is conducted to select the most effective solutions for the allocation of units of RDGs. The efficacy of the utilized techniques is validated through testing on two distinct networks, namely the IEEE 33 and 69 buses RDS in MATLAB, with attainments compared against other techniques. Moreover, a larger RDS system of 118- bus IEEE system has been considered in order to enhance its power quality indices. Moreover, a real case of study from Egypt of 15 bus has been considered and evaluated with considering the applied techniques. The results show the enhancement of the voltage profile and decreasing the power losses of the tested system with the DG systems with the superiority of the MPA and SSA algorithms.

Molecular characterization of the effects of heat shock on the infection cycle progression and productivity of the baculovirus expression vector system

Baculoviruses are widely utilized in biotechnology for various purposes, including recombinant protein expression, antigen presentation, vaccine production, as biopesticides, and as gene therapy vectors. The productivity of the baculovirus expression vector system (BEVS) is significantly affected by the condition of the host cell. However, the impact of host cell stress on the complex baculovirus infection cycle remains not fully understood. This study examines the effects of three gradual heat shock treatments on the production of recombinant protein and viral titers in Sf9 cells (Spodoptera frugiperda) infected with a recombinant baculovirus AcMNPV with fluorescent reporters under late (vp39) and very late (polh) promoters. The heat shock regimens applied before infection were 30°C for 2.5 hours, 37°C for 2.5 hours, and constant 30°C, combined with prostaglandin A1 (PGA1) to enhance the cellular stress response. Significant differences in viral progeny and baculovirus genome replication were observed. Notably, a constant 30°C heat shock increased early viral titers but decreased late-stage yields. Using flow cytometry, we monitored the signal from the two fluorescent reporters and found that some heat shock conditions differentially accelerated or increased their timing or expression levels, with different patterns for each reporter. Additionally we identified, cloned, and sequenced two inducible HSP70 genes from S. frugiperda to track their expression throughout infection, providing insights into the cell’s stress response and the effect of PGA1. These findings suggest that modulating the host heat-shock response can improve baculovirus production and offer insights into the host-virus relationship for new elements or strategies to improve BEVS productivity.

Large scale and regional demographic responses to climatic changes in Europe during the Final Palaeolithic

The European Final Palaeolithic witnessed marked changes in almost all societal domains. Despite a rich body of evidence, our knowledge of human palaeodemographic processes and regional population dynamics still needs to be improved. In this study, we present regionally differentiated population estimates for the Greenland Interstadial 1d-a (GI-1d-a; 14-12.7 ka cal BP) and the Greenland Stadial 1 (GS-1; 12.7-11.6 ka cal BP) for Southern, Western, Northern and Central Europe. The data were obtained by applying the Cologne Protocol, a geostatistical approach for estimating prehistoric population size and density, to a newly compiled dataset of Final Palaeolithic sites. On a large spatio-temporal scale and compared to preceding Upper Palaeolithic phases, areas north of the Alps become the dominant demographic growth area for the first time since the dispersal of anatomically modern humans into Europe. At smaller scales, we observe divergent regional trends, with a conspicuous lack of archaeological evidence appearing in previously occupied areas of central France and Germany. Our study also shows that during the Final Palaeolithic, the climatic cooling of GS-1 coincides with a pronounced population decline in most parts of the study area. An apparent increase in population density occurs only in north-eastern Central Europe and north-eastern Italy. Our estimates suggest that the total population was reduced by half. Similar results, with a relationship between decreasing temperatures and decreasing populations, have already been observed for the late phase of the Gravettian, when populations were reduced to only one third of those estimated for the early phase. Yet, in contrast to the collapse of local populations during the late Gravettian, the increase in population densities in Central Europe during GS-1 indicates population movements eastwards, possibly in response to deteriorating climatic conditions, particularly in western regions during the Younger Dryas.

Soundscape and fish passive acoustic monitoring around a North Sea gas-production platform in the Dogger Bank

This study provides temporo-spatial characterisation of the underwater soundscape in proximity of a relatively newly installed offshore gas-production platform in the North Sea’s Dogger Bank Special Area of Conservation, recorded by Static Acoustic Monitoring at different distances from the wellhead (70 m, 5 Km and 10 km). Long-Term Spectrogram Analysis and percentile Power Spectral Densities demonstrated strong acoustic similarity between sites; no biophonic acoustic-mass phenomena were present. All locations were characterized by Underwater Radiated Noise, concentrated &lt; 2 kHz, which dominated the soundscape. Fish acoustic community analysis was performed to explore occurrence, richness, abundance, diel, and seasonal patterns of putative fish sounds. Principal Component Analysis was used to infer potential sound-emitting species, and was performed on North Sea fish sounds downloaded from the Global Inventory of known fish sounds (https://fishsounds.net/), analyzed for the same acoustic features used to characterize fish sounds recorded during this study. The fish acoustic community was characterized by low levels of diversity (acoustic richness ranging from 1 to 2) and abundance (never above 2 sounds min-1). The fish sound type ‘Pulse Series’ (PS), emitted at the 70 m and at the 5 km station in low abundance in September from ca. 19:00 to 23:00, was characterized by acoustic features with the closest linear combination to those typifying sounds emitted by Eutrigla gurnardus. The fish sound type ‘Low-frequency Down-Sweep’ (LF-DS) was recorded at all stations and was characterized by acoustic features with the closest linear combination to those typifying grunts emitted by Gadus morhua. This study represents the first application of fish acoustic community analysis in the context of environmental management of an operational offshore gas production platform.